Myxoid Stromal Histophenotype Is Associated with High-Grade and Persistent Cervical Intraepithelial Neoplasia

Objectives: To evaluate the diagnostic and prognostic value of histophenotyping of the extracellular matrix of the cervical stroma at cervical intraepithelial neoplasia (CIN). Methods: Retrospective analysis of 160 biopsies and surgical preparations of the cervix in women of reproductive age included cases of CIN 1–3 and the group with confirmed persistence or lesion progression (CIN P) at repeated biopsy. The control group (n = 40) consisted of morphologically intact cervical tissue. Histophenotypes were evaluated by staining with hematoxylin, eosin, and Masson trichrome, and classified as follows: normal (dense parallel bundles of type I collagen), intermediate (disorganized and fragmented type I collagen fibers), and myxoid (amorphous weakly fibrillar matrix). The clinical, viral, and inflammatory characteristics between histophenotypes were statistically compared. Results: The distribution of histophenotypes of the extracellular matrix of the cervix varied significantly depending on the CIN degree (p < 0.001). In the control group, the normal pattern was detected in 97.5% of cases; its frequency decreased from CIN 1 (27.5%) to CIN 2 (12.5%) and was absent at CIN 3. The frequency of the myxoid pattern increased significantly in severe and persistent forms: 55% at CIN 3 and 62.5% at CIN P. Human papillomavirus 16/18 was most frequently detected in groups with intermediate (69.1%) and myxoid (27.2%) patterns. Inflammatory changes were more often accompanied by disorganized extracellular matrix; however, intermediate and myxoid types also occurred in the absence of inflammation. Conclusions: The myxoid histophenotype of the extracellular matrix is significantly associated with the high degree of dysplasia and CIN persistence. It can reflect the morphological equivalent of tumor-associated stroma remodeling. Histophenotyping of the extracellular matrix of the cervix appears to be a promising method of risk stratification and may complement existing diagnostic algorithms for CIN.

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